Superior virological response to boosted protease inhibitor‐based highly active antiretroviral therapy in an observational treatment programme
Bibliographic record
Abstract
BACKGROUND: The use of boosted protease inhibitor (PI)-based antiretroviral therapy has become increasingly recommended in international HIV treatment consensus guidelines based on the results of randomized clinical trials. However, the impact of this new treatment strategy has not yet been evaluated in community-treated cohorts. METHODS: We evaluated baseline characteristics and plasma HIV RNA responses to unboosted and boosted PI-based highly active antiretroviral therapy (HAART) among antiretroviral-naïve HIV-infected patients in British Columbia, Canada who initiated HAART between August 1997 and September 2003 and who were followed until September 2004. We evaluated time to HIV-1 RNA suppression (<500 HIV-1 RNA copies/mL) and HIV-1 RNA rebound (>or=500 copies/mL), while stratifying patients into those that received boosted and unboosted PI-based HAART as the initial regimen, using Kaplan-Meier methods and Cox proportional hazards regression. RESULTS: During the study period, 682 patients initiated therapy with unboosted PI and 320 individuals initiated HAART with a boosted PI. Those who initiated therapy with a boosted PI were more likely to have a CD4 cell count <200 cells/muL and to have a plasma HIV RNA>100 000 copies/mL, and to have AIDS at baseline (all P<0.001). However, when we examined virological response rates, those who initiated HAART with a boosted PI achieved more rapid virological suppression [relative hazard 1.26, 95% confidence interval (CI) 1.06-1.51, P=0.010]. CONCLUSIONS: Patients prescribed boosted PIs achieved superior virological response rates despite baseline factors that have been associated with inferior virological responses to HAART. Despite the inherent limitations of observational studies which require this study be interpreted with caution, these findings support the use of boosted PIs for initial HAART therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".